Medical Laboratory Science
Clinical Laboratory Science
Acute promyelocytic leukemia; microgranular variant; PML-RARα fusion gene; karyotyping; FISH; prognosis
Advancements in medical technology today have positively impacted the diagnosis, treatment, and prognosis of cancers. Particularly acute promyelocytic leukemia (APL) has completely improved from having the poorest prognosis to one of the best. Acute promyelocytic leukemia is a malignant disease of hematopoietic tissue classified by WHO as leukemia with >20% blasts from the myeloid lineage, specifically promyelocytes. Determined in 1976, FAB classified AML subtypes M1-M7, with APL being M3. Specific characteristics classify the subtype of AML, with each resulting from a different genetic abnormality. The focus of APL diagnosis, treatment, and prognosis occurs around the known PML-RARα fusion gene. Flow cytometry, karyotyping, and FISH are all methods done within the lab to determine this genetic abnormality. These molecular methods have been crucial in dictating targeted treatment options for patients and thus improving prognosis. Throughout my internship for medical laboratory science at Rhode Island Hospital I have acquired knowledge and hands-on experience with the process of diagnosing any type of hematological disease. Using this I have accumulated real-life characteristics of acute promyelocytic leukemia and formulated a case study. The purpose of the case study is to demonstrate the process by which APL becomes evident for the patient and how it proceeds through the lab. By understanding the foundation of this disease, the importance of diagnostics comes to light. This case study focuses on a specific type of acute promyelocytic leukemia, the microgranular variant. Each step reviewed from physical examination to molecular methods contributes to prognosis. The goal in presenting this case study, along with research findings, is to emphasize the importance of diagnostic testing and the upcoming positive impacts the growing molecular lab world has on the patient population.